All convolutional layers, excluding the clstm layer and the final two convolutional layers
within the decoder, consist of the following: a convolution operation, a Swish activation function
[1]}, and either instance or batch normalisation. The final two
decoder layers have no normalisation and, as dmri is strictly positive, relu
activation is used in the final layer in place of a Swish activation. The clstm is a 3D
extension of the 2D clstm presented in [2]}, where the dense
connections of a standard lstm cell are replaced with convolutional kernels. It uses standard
activations found within a lstm cell and no normalisation. Each of the convolutional layers,
except for the clstm layer, treat the q-space dimension as an additional batch dimension,
therefore sharing parameters across q-space samples. All convolutional layers use a stride of
\((1\times 1\times 1)\) . Hyperparameters used for each layer can be found in Figure
, and were obtained by a hyperparameter search using KerasTuner
[3]} and the Hyperband algorithm [4]}. Models were
trained for 120 epochs using the optimizer Adam [5]} with mae loss function
and a learning rate of \(0.001\) . The weights used for analysis were the best performing within the
validation dataset. Training and validation datasets comprised of data from 27 and 3 hcp
subjects, respectively.
